Magnetic resonance imaging (MRI) is a non-destructive method for the analysis of materials and represents a new approach to medical imaging. It is generally non-invasive and does not involve ionizing radiation. In very general terms, nuclear magnetic moments are excited at specific spin precession frequencies which are proportional to the local magnetic field. The radio-frequency signals resulting from the precession of these spins are received using pickup coils. By manipulating the magnetic fields, an array of signals is provided representing different regions of the volume. These are combined to produce a volumetric image of the nuclear spin density of the body.
MRI signals for reconstructing an image of an object are obtained by placing the object in a magnetic field, applying magnetic gradients for slice selection, applying a magnetic excitation pulse to tilt nuclei spins in the desired slice or volume, and then detecting MRI signals emitted from the tilted nuclei spins while applying readout gradients. The detected signals may be envisioned as traversing lines in a Fourier transformed space (k-space) with the lines aligned and spaced parallel in Cartesian trajectories or emanating from the origin of k-space in spiral trajectories.
An MRI may be used for scanning a patient's brain or other tissue. The MRI may be useful for measuring development of the brain, particularly for scanning white-matter within the brain. White matter is a component of the central nervous system and consists of myelinated axons. MRI is the preferred reference test for diagnosing and monitoring the evolution of white-matter development and related diseases due to its excellent soft tissue contrast, high spatial resolution, and non-radioactive nature.
In typical MRI systems, phase information present in MRI images are commonly discarded except in a limited number of cases such as measuring of flow in angiography and enhancing image contrast in susceptibility weighted images. Traditionally, phase images are typically noisy and lack tissue contrast, hence these images have limited diagnostic utility. The emerging ultra-high field (7T and higher) MRI have started to reveal more interesting contrast in the phase images with improved signal-to-noise ratio (SNR). Gradient-echo MRI at 7T showed that phase contrast within gray matter exhibited characteristic layered structure. Despite these advances, one intrinsic limitation of signal phase is that phase contrast is non-local, orientation dependent, and thus not easily reproducible. Therefore, it is of great interest to determine the intrinsic property of the tissue, i.e. the magnetic susceptibility, from the measured signal phase.
The quantification of susceptibility from phase images is an ill-posed problem, since the Fourier transform of susceptibility, denoted as χ(k), cannot be accurately determined in regions near conical surfaces defined by k2−3kz2=0. Previous approaches have been proposed to address this issue. For example, threshold techniques have been used to avoid division by zero and approximate the χ(k) values at the two conical surfaces. Although these techniques are often straightforward to implement; the accuracy, however, can be limited. Residual artifacts and noise amplification in the reconstructed susceptibility maps may hamper the visualization of subtle tissue structures, especially at ultra-high resolution. Numerical optimization relying on nonlinear regularization has shown some capability in suppressing the streaking artifacts. Typically, regularized optimization requires a careful choice of the regularization parameters. One common concern is the introduction of excessive external constraints that may cause degradation of intrinsic tissue susceptibility contrast.
For at least the aforementioned reasons, it is desired to provide improved MRI techniques for analyzing brain and other tissues.